The demands on networks are increasing at a fast pace. In particular, real-time applications have very strict network requirements. However, setting up a network that hosts real-time applications is a cost-intensive endeavor, especially for experimental systems such as testbeds. Systems that provide guaranteed real-time networking capabilities usually work with expensive, high-rate software-defined switches. In contrast, real-time networking systems based on low-cost hardware face the limitation of lower link speeds. This paper fills this gap and presents Low-Cost Deterministic Networking (LCDN), a system designed to work with inexpensive, common off-the-shelf switches and devices. LCDN works at Gigabit speed and enables powerful testbeds to host real-time applications with strict delay guarantees. LCDN’s performance is similar to industrial- and production-grade solutions. This paper also provides an evaluation of the determinism of a low-cost switch and a Raspberry Pi used as an end-device to demonstrate the applicability of LCDN for inexpensive, low-power systems.

The rising prevalence of real-time applications that require deterministic communication over mobile networks necessitates the joint operation of both mobile and fixed network components. This joint operation requires designing components that interact between the two technologies to provide users with latency and packet loss guarantees. In this work, we demonstrate a fully integrated 5G-DetNet that can guarantee the end-to-end demands of different flows. Moreover, we show how such a network can be implemented using low-cost hardware and open-source software, making it accessible to many 5G testbeds. The features demonstrated in this work are a network manager that does the routing and scheduling, an application function in the 5G core that interfaces with the network manager, and a network-side translator for user-plane management and de-jittering of the real-time streams.

Time-Sensitive Networking can provide a wide array of QoS guarantees that can be leveraged in a variety of use cases, ranging from industrial applications to autonomous driving. While the control plane can be configured following either a distributed or a centralized paradigm, it is difficult to make general assertions about the affinity of different topologies and use cases towards these configuration methods. In this work, we propose a hybrid simulation of the Resource Allocation Protocol to evaluate the reservation performance across a wide range of network configurations. Results show distinct behaviors between the configuration paradigms, making a case for decentralized control when scalability is critical. In addition, we make our implementation of the developed hybrid model publicly available.

Time-sensitive applications are increasingly prevalent in various network domains, such as industrial, medical, and vehicular communications, imposing substantial demands on network infrastructure. Consequently, ensuring low latency has become a crucial requirement for future networks, particularly through the implementation of deterministic latency network controllers. However, it is essential to recognize that the network controller represents just one facet of network performance management. The configuration of the network’s topology also significantly influences its overall performance. This study, therefore, investigates the impact of different topologies on network performance, specifically focusing on deterministic latency guarantees. Our analysis shows the correlation between graph metrics characterizing the topology and its performance. This correlation facilitates a straightforward ranking of topology performance during critical phases like network planning or expansion. We introduce a readily obtainable graph metric that enables relative performance ranking without the need for exhaustive simulations or emulations. The metric exhibits a Spearman Ranking correlation coefficient exceeding 0.93.

Website Fingerprinting (WFP) attacks infer the websites or webpages a user is visiting from encrypted traffic. To date, it remains uncertain if WFP can attack many users from a central location in an online scenario. We close this gap with PROFI, a WFP attack that detects websites based on the initial TLS connection from the client to the server using at most the connection’s first 30 packets. PROFI achieves a precision and recall of 86.51% and 85.35% in a closed-world, and 68.90% and 78.71% in an open-world scenario, which is competitive to state-of-the-art (SoA) WFP attacks, while taking a fraction of the time of SoA attacks to classify a webpage. Further, we implement PROFI as a micro service-based prototype and evaluate the attack in an online scenario with real traffic traces. We show that PROFI can monitor up to 100 websites at 10 G, corresponding to up to 424 webpages per second. We also show that PROFI has the potential to interfere with a victim’s webpage access.

Traffic Engineering (TE) mechanisms in data center networks make distributed forwarding decisions based on the global network state. Thus, new TE mechanisms require the design and implementation of effective information exchange and efficient decentralized algorithms to compute forwarding decisions, which is challenging and time-intensive. To automate and simplify this process, we proposeMistill.Mistilldistills the forwarding behavior of TE policies from exemplary forwarding decisions into a Neural Network.Mistilllearns (i) how to encode local state into update messages, (ii) which network devices must exchange updates, and (iii) how to map the exchanged updates into forwarding decisions. We demonstrate the abilities ofMistillby learning three TE policies, verifying their performance in simulations on synthetic and real-world traffic patterns, and by showing that the learned policies generalize to unseen traffic patterns. We implementMistillas a proof-of-concept and show thatMistillreacts on average within 1.3ms to changes in the network.

Realizing Service Function Chaining with a micro-service-based architecture results in an increased number of computationally cheap Virtual Network Functions (VNFs). Pinning cheap VNFs to dedicated CPU cores can waste resources since not every VNF fully utilizes its core. Thus, cheap VNFs should share CPU cores to improve resource utilization. However, sharing cores can result in degraded performance due to interference between VNFs, even in mildly loaded scenarios. We proposeD2A, a system that combines Neural Combinatorial Optimization, Machine Learning (ML)-based Digital Twins (DTs), and Game Theory to optimize VNF assignments. Measurements in a testbed show thatD2Aincreases throughput by up to 46% and reduces latency by up to 93%, compared to three baseline algorithms. Using an ML-based DT to model VNF interference increases throughput by up to 11%, and reduces latency by up to 90% compared to an analytical model of the system.